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decision tree iris

Decision tree classification model -- iris data1. Data is read from the table file2. Colors are assigned to different classes of iris -- visualization via scatter plots3. Partitioning to the training data (70%) and testing data (30%)4. Normalization to [0,1]5. Decision tree learner (training the model) and predictor (predictions based on the trained model)6. Model assessment reading thedata tableAssigning colorsfor different irisesTraining 70%Testing 30%Normalizing training datato [0,1]Normalizingtesting datato [0,1]Trainingdecision treemodelApplyingtrained modelto the testing dataScatter plotModelperformance Table Reader Color Manager Partitioning Normalizer Normalizer (Apply) DecisionTree Learner Decision TreePredictor Scatter Plot Scorer ROC Curve Decision tree classification model -- iris data1. Data is read from the table file2. Colors are assigned to different classes of iris -- visualization via scatter plots3. Partitioning to the training data (70%) and testing data (30%)4. Normalization to [0,1]5. Decision tree learner (training the model) and predictor (predictions based on the trained model)6. Model assessment reading thedata tableAssigning colorsfor different irisesTraining 70%Testing 30%Normalizing training datato [0,1]Normalizingtesting datato [0,1]Trainingdecision treemodelApplyingtrained modelto the testing dataScatter plotModelperformance Table Reader Color Manager Partitioning Normalizer Normalizer (Apply) DecisionTree Learner Decision TreePredictor Scatter Plot Scorer ROC Curve

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Created by: Satoru
Created at: 2022-12-06
On NodePit since: 2022-12-06
Last update: 2024-04-27
Created with KNIME version: v4.6.3

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